Abstract The integration of the Internet of Vehicles (IoV) with the Intelligent Transportation System (ITS) plays a significant role in ensuring safe and efficient traffic management. However, communication signals may encounter natural or artificial obstacles that result in uncovered road segments, where connectivity is lost. Even minor collisions in these unprotected areas can escalate into major incidents or traffic bottlenecks, especially when vehicles are traveling at high speeds on highways. Therefore, it is crucial to promptly inform the ITS of any adverse events occurring in these unnoticed areas. The importance of Store-Carry-Forward (SCF) techniques increases in such scenarios when uncovered patches exist and intervehicle distances exceed the communication range. Store-Carry-Forward (SCF) is a technique where vehicles temporarily store data and forward it when a connection is available. To address improved coverage and reduced latency using SCF, we propose a beacon-based Store-Carry-Forward Scheme to Deliver Emergency Messages via vehicle-to-vehicle (V2V) Communication for Highways (SCF-EMD). The proposed method aims to minimize congestion and delays in disseminating information about adverse events. It involves selecting high-mobility vehicles to travel across both covered and uncovered highway segments. The scheme integrates V2V and vehicle-to-infrastructure (V2I) communication to ensure rapid delivery of emergency alerts to ITS. SCF-EMD demonstrated significant improvements over existing schemes in Network Simulator $$(NS-3)$$ , achieving over $$10\%$$ , $$16\%$$ and $$5\%$$ gains in information coverage, end-to-end delay, and packet delivery ratio (PDR), respectively.

Software Defined Networks (SDN) utilize the network devices as virtual devices to analyze the traffic and transmit the relevant packets. It is quite beneficial for the internet of Things (IoT) where the huge number of devices share the packets which cannot be handled by the conventional networks. SDN is also essential to manage congestion scenarios during massive communication especially in video streaming. The main problem arises when the residual queues on the network path are left underutilized whereas the bandwidth is reduced as per the minimum bandwidth link between any two nodes in the path. It occurs based on the feedback based congestion avoidance mechanism. This work presents a proposed mechanism to utilize the residual queue in the communication path to enhance the minimum bandwidth. It maintains the average queue length to analyze the percentage of queue utilized and then decide about the packet dropping probability. In proposed scheme, the congestion can be managed earlier whereas the base scheme applies a constraint of double the size of previous threshold which may result in identifying the congestion at a later stage where the retreat is hardly possible. According to the findings in results after simulations, the proposed IAGRED dominates the counterparts in terms of queue delay, queue length, link utilization and throughput.

Smart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT-enabled wireless sensor networks (WSNs) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This article presents a critical data reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer and data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. The results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.

With the continuous development of Artificial Intelligence (AI), autonomous driving has become a popular research area. AI enables the autonomous driving system to make a judgment, which makes studies on autonomous driving reaches a period of booming development. However, due to the defects of AI, it is not easy to realize a general intelligence, which also limits the research on autonomous driving. In this paper, we summarize the existing architectures of autonomous driving and make a taxonomy. Then we introduce the concept of hybrid human-artificial intelligence (H-AI) into a semi-autonomous driving system. For making better use of H-AI, we propose a theoretical architecture based on it. Given our architecture, we classify and overview the possible technologies and illustrate H-AI’s improvements, which provides a new perspective for the future development. Finally, we have identified several open research challenges to attract the researchers for presenting reliable solutions in this area of research.

Summary Mobile ad hoc Network (MANET) is a cluster of moveable devices connected through a wireless medium to design network with rapidly changing topologies due to mobility. MANETs are applicable in variety of innovative application scenarios where smart devices exchange data among each other. In this case, security of data is the major concern to provide dependable solution to users. This article presents a secure mechanism for data transfer where sender splits the data into fragments and receiver gets the actual data by assimilating the data fragments. We have presented an Enhanced Secured Lempel‐Ziv‐Welch (ES‐LZW) algorithm that provides cryptographic operations for secure data transfer. In proposed model, we have utilized the disjoint paths to transfer the data fragments from sender side and assimilate these fragments at receiver to get the original data. The messages containing data fragments are compressed and encrypted as well. Our scheme ensures confidentiality, integrity, efficient memory utilization, and resilience against node compromising attacks. We have validated our work through extensive simulations in NS‐2.35 using TCL and C language. Results prove that our scheme reduces memory consumption along with less encryption and decryption cost as compared to blowfish especially when plaintext has more repetitive data. We have also analyzed the impact of creating data fragments, fraction of communication compromised, and probability to compromise the data fragments by subverting intermediaries.

With the rapid growth in connected vehicles and related innovative applications, it is getting keen interest among the researchers. Vehicular ad-hoc networks (VANETs) comprise of interconnected vehicles with sensing capabilities to exchange traffic, weather and emergency information. Intelligent transportation system (ITS) supports better coordination among vehicles and in providing reliable services. In VANET, topology is dynamic due to the high mobility of vehicles, therefore, the existing topology-based schemes are not suitable. In this paper, we have explored position based routing (PBR) protocols for VANETs by presenting a taxonomy. The existing survey papers have focused on PBR schemes but we have further focused on considering PBR for the city environment along with connectivity aware routing schemes. Moreover, linear programming, genetic algorithms, and regression-based schemes are also included. To further evaluate the strengths and weaknesses of PBR schemes, a categorical evaluation of different architectures, path strategies, and carry-forward strategies are presented. Currently, no architecture is presented for FoG-oriented VANET using parked vehicles as guards for anchor points or junctions. To fill this aspiring demand, we have presented a FoG-oriented VANET architecture that can support the PBR by utilizing road junctions for path selection. It also involves the vehicles in the parking area for packet transmission. Further, we have proposed to use parked vehicles near junction as an option for selecting guarding vehicle along with ITS as well. It reduces the probability of extensive carry forward-based communication due to the absence of guarding nodes. The records at parked vehicles can be upgraded at neighboring parked vehicles as well during the beaconing or exchange of data messages. The architecture supports better packet delivery ratios, end-to-end delay, transmission time, and communication cost. Moreover, opportunities and challenges of the proposed architecture are also explored to attract researchers toward this area of research.

With the alarming increase in breast cancer cases, researchers have considered it a challenging research problem to propose dependable solutions. It is quite essential for early detection, prevention, and control against breast cancer. Existing schemes still does not utilize recent information technology support, and hence preventive measures and factors are also not appropriate. This paper adopts cloud computing to present association rule-based breast cancer prevention and control system. We have categorized our work into two phases. In phase 1 titled prevention and control, we propose item association rule (IAR) algorithm and N-IAR algorithm for n-item associations. It can be used to discover risk factors for breast cancer. Our algorithm discovers more risk factors than the traditional logistics method. Some factors which can be modified are used for breast cancer prevention and control. In addition, existing risk assessment models are not applicable to Chinese women as well. In phase 2, we manage this by introducing a new model based on machine learning. It utilizes real data from Chinese women and more risk factors for breast cancer. Moreover, we have identified and evaluated a number of new common risk factors. Results prove that our system achieves higher assessment values as compared to preliminaries.

Recently, Internet of Vehicles (IoVs) is getting growing interest because of their suitability for a wide range of emerging applications. Most of these applications require vehicles to continuously update their information to a centralized location in order to gain various services. However, frequent transmission of messages by an abundance number of vehicles may not only overwhelm a centralized server but also causes a huge congestion which might disrupt various services including emergency situations. The aim of this research is to minimize congestion and messaging delay. This paper presents a fog-assisted congestion avoidance scheme for IoV named Energy Efficient Message Dissemination (E2MD). To capitalize the merits of fog computing and minimize latency, E2MD opts a distributed approach by employing a fog server to complement services in IoVs. In E2MD, vehicles continuously update their status to a fog server either directly or through intermediate nodes. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance supremacy of E2MD compared to contemporary schemes in terms of end-to-end delay and messaging cost.

Next generation mobile networks aim to provide faster speed and more capacity along with energy efficiency to support video streaming and massive data sharing in social and communication networks. In these networks, user equipment has to register with IP Multimedia Subsystem (IMS) which promises quality of service to the mobile users that frequently move across different access networks. After each handover caused due to mobility, IMS provides IPSec Security Association establishment and authentication phases. The main issue is that unnecessary reregistration after every handover results in latency and communication overhead. To tackle these issues, this paper presents a lightweight Fast IMS Mobility (FIM) registration scheme that avoids unnecessary conventional registration phases such as security associations, authentication, and authorization. FIM maintains a flag to avoid deregistration and sends a subsequent message to provide necessary parameters to IMS servers after mobility. It also handles the change of IP address for user equipment and transferring the security associations from old to new servers. We have validated the performance of FIM by developing a testbed consisting of IMS servers and user equipment. The experimental results demonstrate the performance supremacy of FIM. It reduces media disruption time, number of messages, and packet loss up to 67%, 100%, and 61%, respectively, as compared to preliminaries.